Overview

Dataset statistics

Number of variables10
Number of observations3051
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory238.5 KiB
Average record size in memory80.0 B

Variable types

Categorical2
Numeric8

Alerts

Calendar_Week has a high cardinality: 113 distinct values High cardinality
Paid_Views is highly correlated with Organic_Views and 1 other fieldsHigh correlation
Organic_Views is highly correlated with Paid_Views and 3 other fieldsHigh correlation
Google_Impressions is highly correlated with Division and 4 other fieldsHigh correlation
Email_Impressions is highly correlated with Division and 4 other fieldsHigh correlation
Facebook_Impressions is highly correlated with Google_Impressions and 2 other fieldsHigh correlation
Affiliate_Impressions is highly correlated with Division and 2 other fieldsHigh correlation
Overall_Views is highly correlated with Paid_Views and 1 other fieldsHigh correlation
Sales is highly correlated with Division and 3 other fieldsHigh correlation
Division is highly correlated with Google_Impressions and 3 other fieldsHigh correlation
Calendar_Week is uniformly distributed Uniform
Email_Impressions has unique values Unique

Reproduction

Analysis started2022-09-20 05:13:18.396358
Analysis finished2022-09-20 05:13:37.564478
Duration19.17 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Division
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
Z
 
226
B
 
113
Y
 
113
X
 
113
W
 
113
Other values (21)
2373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3051
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
Z226
 
7.4%
B113
 
3.7%
Y113
 
3.7%
X113
 
3.7%
W113
 
3.7%
V113
 
3.7%
U113
 
3.7%
T113
 
3.7%
S113
 
3.7%
R113
 
3.7%
Other values (16)1808
59.3%

Length

2022-09-20T10:43:37.761382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
z226
 
7.4%
b113
 
3.7%
c113
 
3.7%
d113
 
3.7%
e113
 
3.7%
f113
 
3.7%
g113
 
3.7%
h113
 
3.7%
i113
 
3.7%
j113
 
3.7%
Other values (16)1808
59.3%

Most occurring characters

ValueCountFrequency (%)
Z226
 
7.4%
B113
 
3.7%
C113
 
3.7%
D113
 
3.7%
E113
 
3.7%
F113
 
3.7%
G113
 
3.7%
H113
 
3.7%
I113
 
3.7%
J113
 
3.7%
Other values (16)1808
59.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3051
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z226
 
7.4%
B113
 
3.7%
C113
 
3.7%
D113
 
3.7%
E113
 
3.7%
F113
 
3.7%
G113
 
3.7%
H113
 
3.7%
I113
 
3.7%
J113
 
3.7%
Other values (16)1808
59.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3051
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z226
 
7.4%
B113
 
3.7%
C113
 
3.7%
D113
 
3.7%
E113
 
3.7%
F113
 
3.7%
G113
 
3.7%
H113
 
3.7%
I113
 
3.7%
J113
 
3.7%
Other values (16)1808
59.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z226
 
7.4%
B113
 
3.7%
C113
 
3.7%
D113
 
3.7%
E113
 
3.7%
F113
 
3.7%
G113
 
3.7%
H113
 
3.7%
I113
 
3.7%
J113
 
3.7%
Other values (16)1808
59.3%

Calendar_Week
Categorical

HIGH CARDINALITY
UNIFORM

Distinct113
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
1/6/2018
 
27
2/9/2019
 
27
8/10/2019
 
27
8/3/2019
 
27
7/27/2019
 
27
Other values (108)
2916 

Length

Max length10
Median length9
Mean length8.92920354
Min length8

Characters and Unicode

Total characters27243
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/6/2018
2nd row1/13/2018
3rd row1/20/2018
4th row1/27/2018
5th row2/3/2018

Common Values

ValueCountFrequency (%)
1/6/201827
 
0.9%
2/9/201927
 
0.9%
8/10/201927
 
0.9%
8/3/201927
 
0.9%
7/27/201927
 
0.9%
7/20/201927
 
0.9%
7/13/201927
 
0.9%
7/6/201927
 
0.9%
6/29/201927
 
0.9%
6/22/201927
 
0.9%
Other values (103)2781
91.2%

Length

2022-09-20T10:43:37.978519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1/6/201827
 
0.9%
1/13/201827
 
0.9%
1/20/201827
 
0.9%
1/27/201827
 
0.9%
2/3/201827
 
0.9%
2/10/201827
 
0.9%
2/17/201827
 
0.9%
2/24/201827
 
0.9%
3/3/201827
 
0.9%
3/10/201827
 
0.9%
Other values (103)2781
91.2%

Most occurring characters

ValueCountFrequency (%)
/6102
22.4%
15400
19.8%
25211
19.1%
03807
14.0%
81944
 
7.1%
91944
 
7.1%
3729
 
2.7%
6567
 
2.1%
7540
 
2.0%
4513
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21141
77.6%
Other Punctuation6102
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15400
25.5%
25211
24.6%
03807
18.0%
81944
 
9.2%
91944
 
9.2%
3729
 
3.4%
6567
 
2.7%
7540
 
2.6%
4513
 
2.4%
5486
 
2.3%
Other Punctuation
ValueCountFrequency (%)
/6102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common27243
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/6102
22.4%
15400
19.8%
25211
19.1%
03807
14.0%
81944
 
7.1%
91944
 
7.1%
3729
 
2.7%
6567
 
2.1%
7540
 
2.0%
4513
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII27243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/6102
22.4%
15400
19.8%
25211
19.1%
03807
14.0%
81944
 
7.1%
91944
 
7.1%
3729
 
2.7%
6567
 
2.1%
7540
 
2.0%
4513
 
1.9%

Paid_Views
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2345
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15093.94166
Minimum1
Maximum518190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:38.207506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile132.5
Q1537
median2699
Q317358
95-th percentile64468
Maximum518190
Range518189
Interquartile range (IQR)16821

Descriptive statistics

Standard deviation30785.88498
Coefficient of variation (CV)2.039618655
Kurtosis45.4428633
Mean15093.94166
Median Absolute Deviation (MAD)2560
Skewness5.199006148
Sum46051616
Variance947770713.9
MonotonicityNot monotonic
2022-09-20T10:43:38.439219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8566
 
0.2%
5006
 
0.2%
1596
 
0.2%
5346
 
0.2%
5545
 
0.2%
2555
 
0.2%
2615
 
0.2%
7295
 
0.2%
2695
 
0.2%
6065
 
0.2%
Other values (2335)2997
98.2%
ValueCountFrequency (%)
13
0.1%
21
 
< 0.1%
32
0.1%
42
0.1%
51
 
< 0.1%
62
0.1%
101
 
< 0.1%
112
0.1%
121
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
5181901
< 0.1%
3610811
< 0.1%
3210821
< 0.1%
3207471
< 0.1%
2585351
< 0.1%
2525451
< 0.1%
2374671
< 0.1%
2104751
< 0.1%
2001441
< 0.1%
1957381
< 0.1%

Organic_Views
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2553
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13355.67322
Minimum1
Maximum270453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:38.662269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile144
Q1712.5
median4110
Q316230.5
95-th percentile54600
Maximum270453
Range270452
Interquartile range (IQR)15518

Descriptive statistics

Standard deviation24079.39969
Coefficient of variation (CV)1.802934176
Kurtosis25.8861535
Mean13355.67322
Median Absolute Deviation (MAD)3814
Skewness4.232216941
Sum40748159
Variance579817489.4
MonotonicityNot monotonic
2022-09-20T10:43:38.905303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2736
 
0.2%
3766
 
0.2%
7395
 
0.2%
9765
 
0.2%
9635
 
0.2%
9925
 
0.2%
6975
 
0.2%
9275
 
0.2%
1575
 
0.2%
8574
 
0.1%
Other values (2543)3000
98.3%
ValueCountFrequency (%)
12
0.1%
32
0.1%
41
 
< 0.1%
53
0.1%
82
0.1%
93
0.1%
102
0.1%
111
 
< 0.1%
121
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
2704531
< 0.1%
2468391
< 0.1%
2456121
< 0.1%
2432451
< 0.1%
2014381
< 0.1%
1992231
< 0.1%
1980411
< 0.1%
1950131
< 0.1%
1900411
< 0.1%
1810241
< 0.1%

Google_Impressions
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2913
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean886173.8309
Minimum7
Maximum17150439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:39.137364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile265
Q1169828
median490531
Q31022621.5
95-th percentile3261280
Maximum17150439
Range17150432
Interquartile range (IQR)852793.5

Descriptive statistics

Standard deviation1355075.817
Coefficient of variation (CV)1.529130933
Kurtosis33.24804215
Mean886173.8309
Median Absolute Deviation (MAD)386893
Skewness4.555611693
Sum2703716358
Variance1.83623047 × 1012
MonotonicityNot monotonic
2022-09-20T10:43:39.366329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1964
 
0.1%
2004
 
0.1%
6834
 
0.1%
1104
 
0.1%
1843
 
0.1%
2343
 
0.1%
5423
 
0.1%
6683
 
0.1%
2263
 
0.1%
5163
 
0.1%
Other values (2903)3017
98.9%
ValueCountFrequency (%)
71
 
< 0.1%
82
0.1%
112
0.1%
161
 
< 0.1%
171
 
< 0.1%
191
 
< 0.1%
201
 
< 0.1%
223
0.1%
251
 
< 0.1%
283
0.1%
ValueCountFrequency (%)
171504391
< 0.1%
164206551
< 0.1%
160017141
< 0.1%
144556231
< 0.1%
139941001
< 0.1%
125567351
< 0.1%
107351721
< 0.1%
106457831
< 0.1%
101410861
< 0.1%
101162821
< 0.1%

Email_Impressions
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3051
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean760509.3778
Minimum40894.44732
Maximum7317730.249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:39.594432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum40894.44732
5-th percentile133318.5653
Q1378496.9247
median590970.802
Q3962246.6105
95-th percentile1895692.071
Maximum7317730.249
Range7276835.802
Interquartile range (IQR)583749.6858

Descriptive statistics

Standard deviation626014.1235
Coefficient of variation (CV)0.8231510904
Kurtosis11.20751273
Mean760509.3778
Median Absolute Deviation (MAD)261356.6528
Skewness2.587596985
Sum2320314112
Variance3.918936829 × 1011
MonotonicityNot monotonic
2022-09-20T10:43:39.838313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
349895.01071
 
< 0.1%
1061981.2321
 
< 0.1%
181384.23731
 
< 0.1%
564751.83481
 
< 0.1%
206812.40451
 
< 0.1%
163574.1011
 
< 0.1%
235901.30051
 
< 0.1%
169795.31931
 
< 0.1%
889099.79841
 
< 0.1%
1286652.2311
 
< 0.1%
Other values (3041)3041
99.7%
ValueCountFrequency (%)
40894.447321
< 0.1%
42547.905031
< 0.1%
52290.500561
< 0.1%
52430.518821
< 0.1%
54682.406741
< 0.1%
55208.458331
< 0.1%
55803.783711
< 0.1%
55964.308241
< 0.1%
56325.096141
< 0.1%
56351.788431
< 0.1%
ValueCountFrequency (%)
7317730.2491
< 0.1%
5160763.7361
< 0.1%
5153551.8691
< 0.1%
5049751.1781
< 0.1%
4723940.2161
< 0.1%
4653501.7741
< 0.1%
4106322.8611
< 0.1%
4088594.1411
< 0.1%
4085227.7671
< 0.1%
4071753.831
< 0.1%

Facebook_Impressions
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3036
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269126.8879
Minimum29
Maximum7558435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:40.070433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile13335.5
Q157073.5
median127523
Q3283505
95-th percentile963280
Maximum7558435
Range7558406
Interquartile range (IQR)226431.5

Descriptive statistics

Standard deviation466511.6667
Coefficient of variation (CV)1.733426453
Kurtosis57.61272328
Mean269126.8879
Median Absolute Deviation (MAD)87736
Skewness5.988878784
Sum821106135
Variance2.176331352 × 1011
MonotonicityNot monotonic
2022-09-20T10:43:40.479081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307802
 
0.1%
821782
 
0.1%
784892
 
0.1%
1255502
 
0.1%
2514882
 
0.1%
4532
 
0.1%
58952
 
0.1%
301402
 
0.1%
2230062
 
0.1%
214622
 
0.1%
Other values (3026)3031
99.3%
ValueCountFrequency (%)
291
< 0.1%
791
< 0.1%
881
< 0.1%
1221
< 0.1%
1341
< 0.1%
1391
< 0.1%
1411
< 0.1%
2181
< 0.1%
2481
< 0.1%
3231
< 0.1%
ValueCountFrequency (%)
75584351
< 0.1%
68302321
< 0.1%
62321181
< 0.1%
45992961
< 0.1%
44059991
< 0.1%
43132211
< 0.1%
42875591
< 0.1%
39946171
< 0.1%
38882241
< 0.1%
37594761
< 0.1%

Affiliate_Impressions
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2936
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22910.90265
Minimum910
Maximum175791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:40.716431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum910
5-th percentile3237
Q19127
median16658
Q327486.5
95-th percentile69527
Maximum175791
Range174881
Interquartile range (IQR)18359.5

Descriptive statistics

Standard deviation21617.6375
Coefficient of variation (CV)0.9435524136
Kurtosis6.882995576
Mean22910.90265
Median Absolute Deviation (MAD)8472
Skewness2.316177224
Sum69901164
Variance467322250.9
MonotonicityNot monotonic
2022-09-20T10:43:40.943357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89543
 
0.1%
125333
 
0.1%
135572
 
0.1%
180822
 
0.1%
234932
 
0.1%
235662
 
0.1%
335452
 
0.1%
258982
 
0.1%
235792
 
0.1%
176512
 
0.1%
Other values (2926)3029
99.3%
ValueCountFrequency (%)
9101
< 0.1%
9121
< 0.1%
10551
< 0.1%
10621
< 0.1%
10891
< 0.1%
11391
< 0.1%
12471
< 0.1%
12851
< 0.1%
13091
< 0.1%
13171
< 0.1%
ValueCountFrequency (%)
1757911
< 0.1%
1564101
< 0.1%
1523421
< 0.1%
1436561
< 0.1%
1436401
< 0.1%
1410401
< 0.1%
1369561
< 0.1%
1344501
< 0.1%
1318271
< 0.1%
1310551
< 0.1%

Overall_Views
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2601
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27980.91413
Minimum2
Maximum635057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:41.168478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile164.5
Q1747
median7879
Q334111.5
95-th percentile115101
Maximum635057
Range635055
Interquartile range (IQR)33364.5

Descriptive statistics

Standard deviation52054.97669
Coefficient of variation (CV)1.860374413
Kurtosis25.62023576
Mean27980.91413
Median Absolute Deviation (MAD)7564
Skewness4.212281555
Sum85369769
Variance2709720598
MonotonicityNot monotonic
2022-09-20T10:43:41.399243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7765
 
0.2%
4915
 
0.2%
8695
 
0.2%
8875
 
0.2%
9195
 
0.2%
7755
 
0.2%
3925
 
0.2%
9885
 
0.2%
9934
 
0.1%
8044
 
0.1%
Other values (2591)3003
98.4%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
42
0.1%
51
 
< 0.1%
61
 
< 0.1%
73
0.1%
82
0.1%
112
0.1%
121
 
< 0.1%
134
0.1%
ValueCountFrequency (%)
6350571
< 0.1%
5639911
< 0.1%
4625121
< 0.1%
4465891
< 0.1%
4384771
< 0.1%
4365071
< 0.1%
4325851
< 0.1%
3883221
< 0.1%
3860441
< 0.1%
3810291
< 0.1%

Sales
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3030
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185901.3966
Minimum15436
Maximum3575430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2022-09-20T10:43:41.635437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15436
5-th percentile44684
Q173393.5
median113573
Q3202975.5
95-th percentile588421.5
Maximum3575430
Range3559994
Interquartile range (IQR)129582

Descriptive statistics

Standard deviation232207.9011
Coefficient of variation (CV)1.249091752
Kurtosis54.06561259
Mean185901.3966
Median Absolute Deviation (MAD)49231
Skewness5.622929513
Sum567185161
Variance5.392050934 × 1010
MonotonicityNot monotonic
2022-09-20T10:43:41.873327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
737132
 
0.1%
1160502
 
0.1%
725042
 
0.1%
763082
 
0.1%
1017562
 
0.1%
1826032
 
0.1%
769572
 
0.1%
722692
 
0.1%
745902
 
0.1%
599742
 
0.1%
Other values (3020)3031
99.3%
ValueCountFrequency (%)
154361
< 0.1%
184321
< 0.1%
184401
< 0.1%
198991
< 0.1%
203771
< 0.1%
208221
< 0.1%
208491
< 0.1%
210921
< 0.1%
215961
< 0.1%
217721
< 0.1%
ValueCountFrequency (%)
35754301
< 0.1%
35612921
< 0.1%
33227581
< 0.1%
24241241
< 0.1%
23632721
< 0.1%
18977381
< 0.1%
18595411
< 0.1%
17917131
< 0.1%
17563871
< 0.1%
17127921
< 0.1%

Interactions

2022-09-20T10:43:35.288981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:22.856602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:24.690329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:26.367413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:28.233075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:29.983467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:31.692379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:33.538909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:35.494500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:23.099857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:24.907848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:26.574195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:28.439410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:30.191106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:31.907483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:33.736521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:35.717372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:23.324297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:25.112592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:26.787536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:28.646611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:30.400548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:32.266548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:33.943269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:35.924544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:23.546294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:25.323029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:27.013381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:28.856574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:30.621531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:32.484302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:34.161597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:36.141170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:23.784383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:25.533544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:27.221933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:29.072490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:30.823352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:32.699137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:34.377504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:36.346558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:24.010210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:25.737526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:27.575961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:29.288575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:31.037505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:32.903549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:34.590534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:36.566592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:24.219533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:25.949478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:27.799379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:29.500568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:31.253631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:33.118034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:34.807263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:36.934140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:24.458893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:26.165942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:28.020633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:29.735929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:31.478125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:33.326477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T10:43:35.051608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-20T10:43:42.091040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-20T10:43:42.341516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-20T10:43:42.572512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-20T10:43:42.811517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-20T10:43:37.256127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-20T10:43:37.505266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DivisionCalendar_WeekPaid_ViewsOrganic_ViewsGoogle_ImpressionsEmail_ImpressionsFacebook_ImpressionsAffiliate_ImpressionsOverall_ViewsSales
0A1/6/2018392422408349895.0107735801207268259417
1A1/13/2018787904110506270.217611804949985356806
2A1/20/201881970742430042.1538522321704875948715
3A1/27/20182557565417745.6658786401020794272047
4A2/3/2018565284295408505.801240561583465856235
5A2/10/2018256330683434729.755036750846969156347
6A2/17/201888656664634432.9117112489833168581604
7A2/24/201833699470555036.3088218631956980492
8A3/3/2018305209501423690.083713065789877261804
9A3/10/2018955283609471730.039084449842883364944

Last rows

DivisionCalendar_WeekPaid_ViewsOrganic_ViewsGoogle_ImpressionsEmail_ImpressionsFacebook_ImpressionsAffiliate_ImpressionsOverall_ViewsSales
3041Z12/28/20198438171435982003.897592e+05111386848424536120823
3042Z1/4/202033345210753065324.496571e+05110314948553762132942
3043Z1/11/20208568251402898945.970322e+0514993098363398894164
3044Z1/18/202017725232743277765.656911e+051588961750140339104771
3045Z1/25/202023817221345606214.684737e+05123430134744496777487
3046Z2/1/202029239253116224061.459071e+0645026120985366782707
3047Z2/8/202026230280316244095.342505e+0522707095485366584503
3048Z2/15/202024749312814393624.227182e+05393685986155561147325
3049Z2/22/202020713303564641786.085799e+054246761022149221111525
3050Z2/29/202015990269934490324.390165e+05161439102944299498187